BlueBear platform

An enterprise AI agent platform for governed production work

Explore BlueBear features for MCP-connected agents, voice agents, workflow automation, Budget Manager controls, access rights, model routing, and Bring Your Own Cloud deployments.

How BlueBear handles the work

Governed workspaces

Users, agents, integrations, workflows, budgets, and evidence are scoped to tenant and workspace boundaries, so one client engagement cannot reach another one.

Evidence: Tenant and workspace scoping, membership rights, per-workspace budgets

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MCP-connected tool access

Approved connections and permitted actions are evaluated at an execution boundary, so tool authority stays outside prompt text instead of travelling in model context.

Evidence: Assigned connections, permitted actions, policy results, credential references

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Approvals and risk tiering

Irreversible, sensitive, outbound, and budget-affecting actions can require scoped human approval, while low-risk steps proceed automatically and stay recorded.

Evidence: Approval gates, decision context, requester and approver identity

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Routing, cost, and evidence

Routing policy selects models per workload rather than hard-coding a provider, and sessions correlate usage, retries, approvals, and outcomes for review.

Evidence: Model routes, session usage, retries, accepted outcomes, per-tenant cost

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Deployment options

The same governed platform runs on managed infrastructure, in a customer-owned cloud account, or in a private environment, with responsibilities agreed per pattern.

Evidence: Managed, BYOC, and private deployment paths with explicit operating duties

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From request to inspectable outcome

  1. Pick one workflow

    Name the business outcome, its owner, the systems involved, the sensitivity of the data, and what counts as an accepted result.

  2. Draw the boundary

    Choose the tenant, workspace, identities, MCP tools, permitted actions, models, and budget the work is allowed to touch.

  3. Gate what matters

    Require human approval on irreversible, outbound, and budget-affecting steps, and let the rest run while remaining recorded.

  4. Review the evidence

    Inspect model routes, tool calls, approvals, retries, failures, cost, and outcomes, then adjust policy from what the record shows.

Frequently asked questions

What is BlueBear?
BlueBear is a governed AI agent platform. It provides the workspaces, tool connections, approval controls, evidence, routing, and deployment paths needed to run agents in production, rather than a library for writing agent code.
How is a platform different from an agent framework?
A framework accelerates building one agent inside an application. A platform operates many agents across teams or clients, which requires shared identity, tool governance, approvals, cost attribution, evidence, and a deployment story that survives a production review.
Does BlueBear replace the systems we already run?
No. BlueBear connects to existing business systems, databases, and tools through Model Context Protocol connections and scoped actions, so agents operate around current systems of record instead of replacing them.
Can agents take action without a human approving it?
Approval requirements are configured per action. Irreversible, sensitive, outbound, or budget-affecting actions can require scoped human approval, while low-risk steps proceed automatically and remain recorded in session evidence.
Which models can BlueBear use?
BlueBear routes across multiple model providers using a routing policy rather than a hard-coded provider, so quality, cost, latency, and availability can be balanced per workload. Available providers for a given deployment are confirmed during setup.